Data is everywhere these days, and there is a clear financial opportunity to turn complex information into actionable insights. Allied Market Research forecasts that insights from data will likely produce more than $100 billion in revenue worldwide by 2023. Colleges and universities need to consider how they can join this data economy, as fully harnessing their massive volumes of data can help schools address some of their biggest challenges, including student recruitment and retention, student engagement, curriculum optimization, university reputation, and overall costs. But before higher education institutions kick off complex data analytics programs, they need to take the time to establish a strategy and follow industry best practices.
One of the most common mistakes made by companies starting analytics programs is to hire a data expert, shove him/her in front of a database, and expect magic to happen. Without a strategic plan, however, a lot of projects can fail.
Any higher education technology rollout needs to start with strategy. Decision-makers must define the issues they intend to explore and address with data. Objectives vary by university, but some of the common ones include improving student recruitment, boosting student retention, increasing revenue generated by donors and tuition, optimizing or personalizing curricula, bolstering reputation, and streamlining academic or business processes, such as financial aid document processing and awards.
Once problems are identified, organizations can choose how they want to measure outcomes. Key performance indictors (KPIs) are summary measurements of a goal that can be tracked over time to measure progress or improvement. Many times, these can be summarized visually or plotted on a graph that follows the measurement through a given time period.
One of the biggest hurdles in getting started in analytics is sifting through the buzzwords and figuring out which tools best align with a university. Let’s go through some of the common buzzwords and define them.
Identifying a tool that aligns with a school’s needs is one of the biggest challenges associated with leveraging analytics in higher education. Before you think about what toolset you should be even considering, you first need to:
Insight based on data is powerful, but it will likely fail to introduce cost-saving or revenue-boosting capabilities if higher education institutions do not revisit processes or policies.
Once something actionable is identified within an analytics project, the next challenge involves designing a trial to test different solutions to the problem using data. One way to generate ideas is to review the published literature on the problem.
Understanding best practices and sifting through buzzwords to find the best analytics solutions can go a long way in deriving value from data.
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